A novel statistical approach shows evidence for multi-system physiological dysregulation during aging

Groupe de recherche PRIMUS, Dept. of Family Medicine, University of Sherbrooke, CHUS-Fleurimont, 3001 12(e) Ave N, Sherbrooke, QC J1H 5N4, Canada. Electronic address: .
Mechanisms of ageing and development (Impact Factor: 3.4). 01/2013; 134(3). DOI: 10.1016/j.mad.2013.01.004
Source: PubMed


Previous studies have identified many biomarkers that are associated with aging and related outcomes, but the relevance of these markers for underlying processes and their relationship to hypothesized systemic dysregulation is not clear. We address this gap by presenting a novel method for measuring dysregulation via the joint distribution of multiple biomarkers and assessing associations of dysregulation with age and mortality. Using longitudinal data from the Women's Health and Aging Study, we selected a 14-marker subset from 63 blood measures: those that diverged from the baseline population mean with age. For the 14 markers and all combinatorial sub-subsets we calculated a multivariate distance called the Mahalanobis distance (MHBD)(1) for all observations, indicating how "strange" each individual's biomarker profile was relative to the baseline population mean. In most models, MHBD correlated positively with age, MHBD increased within individuals over time, and higher MHBD predicted higher risk of subsequent mortality. Predictive power increased as more variables were incorporated into the calculation of MHBD. Biomarkers from multiple systems were implicated. These results support hypotheses of simultaneous dysregulation in multiple systems and confirm the need for longitudinal, multivariate approaches to understanding biomarkers in aging.

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Available from: Emmanuel Milot
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    • "Correlation between dysregulation scores of a priori systems Previous studies (Cohen et al., 2013, 2014; Milot et al., 2014b) showed that global dysregulation increased with age, and the current study confirms that this is also true for system-specific dysregulation (see 'association of dysregulation with age'). Accordingly, correlations among dysregulation scores of different systems might be due solely to the fact that each correlates with age, rather than to an independent biological link in the dysregulation rates. "
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    ABSTRACT: An increasing number of aging researchers believes that multi-system physiological dysregulation may be a key biological mechanism of aging, but evidence of this has been sparse. Here, we used biomarker data on nearly 33 000 individuals from four large datasets to test for the presence of multi-system dysregulation. We grouped 37 biomarkers into six a priori groupings representing physiological systems (lipids, immune, oxygen transport, liver function, vitamins, and electrolytes), then calculated dysregulation scores for each system in each individual using statistical distance. Correlations among dysregulation levels across systems were generally weak but significant. Comparison of these results to dysregulation in arbitrary 'systems' generated by random grouping of biomarkers showed that a priori knowledge effectively distinguished the true systems in which dysregulation proceeds most independently. In other words, correlations among dysregulation levels were higher using arbitrary systems, indicating that only a priori systems identified distinct dysregulation processes. Additionally, dysregulation of most systems increased with age and significantly predicted multiple health outcomes including mortality, frailty, diabetes, heart disease, and number of chronic diseases. The six systems differed in how well their dysregulation scores predicted health outcomes and age. These findings present the first unequivocal demonstration of integrated multi-system physiological dysregulation during aging, demonstrating that physiological dysregulation proceeds neither as a single global process nor as a completely independent process in different systems, but rather as a set of system-specific processes likely linked through weak feedback effects. These processes - probably many more than the six measured here - are implicated in aging.
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    • "Many of our analyses have been replicated across a very large number of biomarker combinations. Our initial analyses used a group of 14 markers identified through a statistical selection procedure, and were replicated on every combination of these 14, i.e. 16 383 combinations (Cohen et al. 2013), (2014). We then used the full set of 44, testing 5000 random combinations for each possible number between 1 and 44, or all combinations when less than 5000 existed (Cohen et al. 2015a). "
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    ABSTRACT: There have long been suggestions that aging is tightly linked to the complex dynamics of the physiological systems that maintain homeostasis, and in particular to dysregulation of regulatory networks of molecules. This review synthesizes recent work that is starting to provide evidence for the importance of such complex systems dynamics in aging. There is now clear evidence that physiological dysregulation-the gradual breakdown in the capacity of complex regulatory networks to maintain homeostasis-is an emergent property of these regulatory networks, and that it plays an important role in aging. It can be measured simply using small numbers of biomarkers. Additionally, there are indications of the importance during aging of emergent physiological processes, functional processes that cannot be easily understood through clear metabolic pathways, but can nonetheless be precisely quantified and studied. The overall role of such complex systems dynamics in aging remains an important open question, and to understand it future studies will need to distinguish and integrate related aspects of aging research, including multi-factorial theories of aging, systems biology, bioinformatics, network approaches, robustness, and loss of complexity.
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    • "This would result in substantially weakened signals for each marker alone, but perhaps a stronger signal for appropriate composite measures. The A/C imbalance score we used here offers one possible explanation for the confidence intervals, but it is possible that other scores are more informative, such as a statistical distance among markers (Cohen et al. 2013). Which summary measures perform best may indeed provide information about the nature of the underlying processes. "
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